code: def _ablation_test_assert(
      self,
      model: Module,
      layer: Module,
      test_input: Union[Tensor, Tuple[Tensor, ...]],
      expected_ablation: Union[List, Tuple],
      layer_mask: Union[None, Tensor, Tuple[Tensor, ...]] = None,
      additional_input: Any = None,
      perturbations_per_eval: Tuple[int, ...] = (1,),
      baselines: BaselineType = None,
      target: Union[None, int] = 0,
      attribute_to_layer_input: bool = False,
  ) -> None:
      for batch_size in perturbations_per_eval:
          ablation = LayerFeatureAblation(model, layer)
          attributions = ablation.attribute(
              test_input,
              target=target,
              layer_mask=layer_mask,
              additional_forward_args=additional_input,
              layer_baselines=baselines,
              perturbations_per_eval=batch_size,
              attribute_to_layer_input=attribute_to_layer_input,
          )
          assertTensorTuplesAlmostEqual(
              self, attributions, expected_ablation
          )
  
file_path: captum/tests/attr/layer/test_layer_ablation.py
imports: import unittest
  from typing import Any, List, Tuple, Union
  import torch
  from captum._utils.typing import BaselineType
  from captum.attr._core.layer.layer_feature_ablation import LayerFeatureAblation
  from tests.helpers.basic import assertTensorTuplesAlmostEqual, BaseTest
  from tests.helpers.basic_models import BasicModel_ConvNet_One_Conv, BasicModel_MultiLayer, BasicModel_MultiLayer_MultiInput
  from torch import Tensor
  from torch.nn import Module
label_desc: Use image node_id 7 for calling the Test obj's underlying member method code with example usage: obj._ablation_test_assert(model, layer, test_input, expected_ablation, layer_mask, additional_input, perturbations_per_eval, baselines, target, attribute_to_layer_input) without return types